(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 11, 2017 548 | Page www.ijacsa.thesai.org Investigating Clinical Decision Support Systems Success Factors with Usability Testing Vitri Tundjungsari Faculty of Information Technology YARSI University Jl. Letjen Suprapto, Jakarta, Indonesia Abdul Salam Mudzakir Sofro Faculty of Medicine YARSI University Jl. Letjen Suprapto, Jakarta, Indonesia Ahmad Sabiq Faculty of Information Technology YARSI University Jl. Letjen Suprapto, Jakarta, Indonesia Aan Kardiana Faculty of Information Technology YARSI University Jl. Letjen Suprapto, Jakarta, Indonesia Abstract—Clinical Decision Support Systems (CDSS) have been used widely since 2000s to improve the healthcare quality. CDSS can be utilized to support healthcare services as a tool to diagnose, predict, as well as to provide clinical interpretation, alert, and reminder. There are many researches of CDSS implementation on literatures but not many of them present the evidence of CDSS successful implementation. In spite of the potential use of CDSS, there are some researches that reveal the failures of CDSS implementation. This paper contributes to CDSS development by investigating and exploring CDSS success factors with usability testing. The testing involves participants from different types of backgrounds (physicians, IT developers, and students). The participants are being asked to experience three different CDSS to predict cardiovascular risk factors. The result of the research shows that involving different type of users give more insight to design process. It can be concluded that user center design is very critical to produce successful CDSS. Keywords—Clinical decision support systems; success factors; user; usability testing I. INTRODUCTION The background of this research starts from the widespread development and use of information technology to support decision making in the health field, also called as: Clinical Decision Support System (CDSS). This study aims to find out the understanding of CDSS from the perspectives of physicians (such as: doctors and prospective doctors/medical faculty students) as well as understanding from the information technology staffs (such as: IT developers, lecturers and students of information technology department). We also investigate the public’s understanding of clinical decision support system. The physician/medical staff is chosen as representative of the experts from the content, i.e. health perspectives, while IT developers as the representatives of expert who develop the CDSS. This research is important to find out the extent to which CDSS usage and benefits from participants’ point of view. The focus of CDSS application in this study is for the detection of chronic diseases, i.e.: cardiovascular disease. Chronic diseases provide a greater public health burden than acute illness because it requires more visits and medications [1]. Thus the use of CDSS for chronic diseases prediction is expected to reduce the cost of treatment and finally can decrease the mortality caused by chronic diseases. In this paper we compare three CDSS to predict cardiovascular risk using usability testing. Those three CDSS have different interface and indicators to perform calculation of cardiovascular risk factors. The rest of the paper is organized as follows. Section 2 provides a brief review of existing CDSS research. Section 3 presents the method used in this research and Section 4 describes our findings. Finally in Section 5, we conclude the paper and state our future work. II. CLINICAL DECISION SUPPORT SYSTEM A. CDSS Application CDSS is a tool with electronic media used to determine diagnosis, clinical interpretation, trends, alerting, reminder, predictive analysis with applications (services or interfaces) which is connected to the data. Another definition of CDSS is system that provides information to medical personnel, patients or individuals or populations, to produce faster, more efficient, better health outcomes for both individual health services and the health of a population [2]. From the above definition, it can be concluded that CDSS has the main objective to support various clinical functions, such as: providing documentation and clinical coding, organizing clinical complexity, storing and maintaining patient databases, tracking patient orders, monitoring and tracking health condition, as well as used for preventive measures disease. In the following discussion, we present some examples of CDSS applications that have been developed and implemented in the real world, as follows:  ATHENA. Athena is a CDSS application developed in 2002 as a tool to provide guidelines for people with hypertension. Athena helps patients in controlling blood pressure and recommends appropriate treatment options for patients. Athena also provides information